Applying Probabilistic Inference to Heuristic Search by Estimating Variable Bias

Eric I. Hsu, Christian J. Muise, J. Christopher Beck, Sheila McIlraith

Backbone variables have the same assignment in all solutions to a given constraint satisfaction problem; more generally, bias represents the proportion of solutions that assign a variable a particular value. Intuitively such constructs would seem important to efficient search, but their study to date has assumed a mostly conceptual perspective, in terms of indicating problem hardness or motivating and interpreting heuristics. In this work, we first measure the ability of both existing and novel probabilistic message-passing techniques to directly estimate bias (and identify backbones) for the specific problem of Boolean Satisfiability (SAT). We confirm that methods like Belief Propagation and Survey Propagation — plus Expectation Maximization-based variants — do produce good estimates with distinctive properties. We demonstrate the use of bias estimation within a modern SAT solver, and exhibit a correlation between accurate, stable, estimates and successful search. The same process also yields a family of search heuristics that can dramatically improve search efficiency for the hard random problems considered in this study.

Subjects: 15.7 Search; 3.4 Probabilistic Reasoning

Submitted: May 1, 2008


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